本文主要參考了：https://blog.csdn.net/heimu24/article/details/53581362

                    https://blog.csdn.net/gaohuazhao/article/details/69568267

五、構建網路

在myfile4下建立一個.prototxt檔案，來搭建網路。檔名為：myfile4_train_test.prototxt  構建的網路內容如下：

name: "myfile4"
layer {
  name: "data"
  type: "Data"
  top: "data"
  top: "label"
  include {
    phase: TRAIN
  }
  transform_param{
  mean_file:"examples/myfile4/mean.binaryproto"
 }
  data_param {
    source: "examples/myfile4/img_train_lmdb"
    batch_size: 50
    backend: LMDB
  }
}
layer {
  name: "data"
  type: "Data"
  top: "data"
  top: "label"
  include {
    phase: TEST
  }
  transform_param{
  mean_file:"examples/myfile4/mean1.binaryproto"
 }
  data_param {
    source: "examples/myfile4/img_val_lmdb"
    batch_size: 50
    backend: LMDB
  }
}
layer {
  name: "conv1"
  type: "Convolution"
  bottom: "data"
  top: "conv1"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  convolution_param {
    num_output: 32
    pad:2
    kernel_size: 5
    stride: 1
    weight_filler {
      type:"gaussian"
      std:0.0001
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "conv1"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layer {
   name:"relu1"
   type:"ReLU"
   bottom:"pool1"
   top:"pool1"
}
layer {
  name: "conv2"
  type: "Convolution"
  bottom: "pool1"
  top: "conv2"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  convolution_param {
    num_output: 32
    pad:2
    kernel_size: 5
    stride: 1
    weight_filler {
      type: "gaussian"
      std:0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
    name:"relu2"
    type:"ReLU"
    bottom:"conv2"
    top:"conv2"
}
layer {
  name: "pool2"
  type: "Pooling"
  bottom: "conv2"
  top: "pool2"
  pooling_param {
    pool: AVE
    kernel_size: 3
    stride: 2
  }
}
layer {
   name:"conv3"
   type:"Convolution"
   bottom:"pool2"
   top:"conv3"
   param{
     lr_mult:1
  }
   param{
     lr_mult:2
  }
  convolution_param {
     num_output:64
     pad:2
     kernel_size:5
     stride:1
     weight_filler {
       type:"gaussian"
       std:0.01
   }
    bias_filler{
      type:"constant"
    }
  }
}
layer {
  name:"relu3"
  type:"ReLU"
  bottom:"conv3"
  top:"conv3"
}
layer {
  name:"pool3"
  type:"Pooling"
  bottom:"conv3"
  top:"pool3"
  pooling_param {
    pool:AVE
    kernel_size:3
    stride:2
  }
}
layer {
  name: "ip1"
  type: "InnerProduct"
  bottom: "pool3"
  top: "ip1"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  inner_product_param {
    num_output: 64
    weight_filler {
      type: "gaussian"
      std:0.1
    }
    bias_filler {
      type: "constant"
    }
  }
}

layer {
  name: "ip2"
  type: "InnerProduct"
  bottom: "ip1"
  top: "ip2"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  inner_product_param {
    num_output: 10
    weight_filler {
      type: "gaussian"
      std:0.1
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name:"accuracy"
  type:"Accuracy"
  bottom:"ip2"
  bottom:"label"
  top:"accuracy"
  include {
  phase:TEST
  }
}
layer {
  name: "loss"
  type: "SoftmaxWithLoss"
  bottom: "ip2"
  bottom: "label"

}

接下來要設定網路的訓練形式，也是建立一個.prototxt檔案。檔名為myfile4_solver.prototxt，內容如下：

net: "examples/myfile4/myfile4_train_test.prototxt"
test_iter: 2
test_interval: 50
base_lr: 0.001
lr_policy: "step"
gamma: 0.1
stepsize:400
momentum:0.9
weight_decay:0.004
display:10
max_iter: 2000
snapshot: 2000
snapshot_prefix: "examples/myfile4"
solver_mode: CPU

在caffe的根目錄下執行 build/tools/caffe train -solver examples/myfile4/myfile4_solver.prototxt

就會得到想要的模型

注意：有的訓練網路TEST，有的訓練網路不帶測試，就是加不加下面這段

如果不加這段的網路，需要在solver檔案中把有關測試的設定去掉否則會出錯。需要去掉的語句是test_iter還有test_interval等語句。

至此訓練也已經完成，接下來需要做的就是用已經訓練好的模型去識別想要識別的資料。